간행물
한국산업경영시스템학회지 KCI 등재 Journal of Society of Korea Industrial and Systems Engineering
- 발행기관 한국산업경영시스템학회
- 자료유형 학술지
- 간기 계간
- ISSN 2005-0461 (Print)2287-7975 (Online)
- 수록기간 1978 ~ 2025
- 주제분류 공학 > 산업공학 공학 분류의 다른 간행물
- 십진분류KDC 530DDC 620
권호리스트/논문검색
Vol.48 No.4 (2025년 12월) 15건
1.
2025.12
구독 인증기관 무료, 개인회원 유료
This study designed an integrated certification system of ISO 50001 (energy management system) and ISO 14001 (environmental management system) with the goal of reducing corporate carbon emissions, and analyzed cases of applying it to actual management. In order to overcome the limitations of individual certification, an integrated operating system of the two standards was established, and a method was sought to maximize corporate energy efficiency and carbon emission management performance. After introducing the integrated system, the company under study reduced energy consumption by up to 30%, and carbon emissions were also significantly reduced. These results were achieved through internal process improvement and supply chain collaboration. This study suggests that the ISO integrated certification system can contribute to cost reduction and sustainable management, and proposes an implementation model that can be utilized in various industries in the future.
4,600원
2.
2025.12
구독 인증기관 무료, 개인회원 유료
This study proposes a Bayesian framework for sequential stock investment decision-making using daily high and low stock price data. The proposed methodology models stock behavior using the Beta distribution and constructs a prior Normal-Gamma distribution based on the derived mean and variance. Additional parameters are estimated from the observed stock price range to enhance the framework's adaptability. The methodology establishes two Bayesian control charts that simultaneously monitor investment performance (Expected Stock Performance Control Chart, ESCFCC) and volatility (Variability of Stock Performance Control Chart, VSCFCC). These control charts are periodically updated with observed data and iteratively revise the posterior probability distribution as new data becomes available. This updating procedure provides investors with timely and data-driven decision-making information. For empirical validation, four investment scenarios were analyzed based on Samsung Electronics stock data from January 4, 2010, to May 31, 2017. The results highlight the usability of a sequential Stock Cash Flow Control Chart (SCFCC) framework, which utilizes daily high and low stock price data to enable real-time evaluation of investment performance and risk. By integrating statistical quality control charts with Bayesian probabilistic models, the framework establishes a system for continuously updating investment information and dynamically monitoring performance throughout the investment period.
4,000원
3.
2025.12
구독 인증기관 무료, 개인회원 유료
Anomaly detection is crucial for ensuring the reliability and safety of mechanical systems across industries such as power generation, manufacturing, and transportation. In these mechanical systems, data is usually collected in time-series form using sensors such like vibration, current or sound for anomaly detection. Time-series anomaly detection methods often face limitations due to insufficient training data and poor generalization across complex operating conditions and varying loads. To address these challenges, this study proposes a transfer learning-based anomaly detection model, leveraging pre-trained knowledge to deliver robust performance and adaptability in data-scarce scenarios and diverse industrial environments. To this end, time-series signals are transformed into spectrograms through Short-Time Fourier Transform(STFT), followed by feature extraction through a Convolutional Autoencoder to obtain low-dimensional latent features. These features are used to detect anomalies using classification such as Random Forest and eXtreme Gradient Boosting. Building on this approach, this research validates the model's performance through migration tasks using the Case Western Reserve University(CWRU) Bearings dataset. Furthermore, to show cross-condition generalization, the proposed model was validated on the Hanoi University of Science and Technology(HUST), Sumair–Umar Bearing Fault(SUBF) dataset v2.0, and a dataset collected using microphone sensor in motor dynamo tests. Consequently, unlike other studies limited by specific operating conditions, the proposed model exhibits strong generalization performance across benchmark datasets. Experimental results highlight the effectiveness of combining STFT, CAE, and tree-based classifiers in addressing data scarcity and enhancing generalization, making it highly suitable for real-world industrial applications. Future work will focus on noise-robust techniques and broader fault types to further improve performance.
4,000원
4.
2025.12
구독 인증기관 무료, 개인회원 유료
The TDL (Tactical Data Link) network employs a TDMA (Time Division Multiple Access) scheme, in which the network resources of each wireless node are allocated to time slots. The method of time slot allocation for each node is based on expert experience and operational requirements. However, this method has limitations because it is difficult to verify real-world operational environments due to high costs and time requirements. To address these limitations, this study developed a TDMA simulator using SimPy, a Python-based discrete-event simulation framework. The proposed simulator enables analysis of time slot allocation methods under varying operational environment conditions. Simulation experiments were conducted to evaluate times slot requirements under different maximum message transmission delay time thresholds (6s and 12s). The results showed that stricter delay time thresholds and higher number of tracks increased the required number of time slots. In addition, the required number of time slots increased differently depending on the complex interaction of factors such as the number of tracks, delay time thresholds, operational scenarios. The proposed simulator provides more precise insights and supports more reliable TDL network design than conventional methods.
4,000원
5.
2025.12
구독 인증기관 무료, 개인회원 유료
This study aims to propose improvement measures and curriculum development strategies to maximize the educational effectiveness of the workforce training program conducted within the Carbon Materials Specialized Materials-Parts-Equipment (MPE) Cluster. An analysis of the relationship between educational satisfaction and skill improvement rates, based on participants’ characteristics— such as age, educational background, and job position—revealed a significant positive correlation between these variables. In particular, program content, training facilities and environment, and operation time were found to have a statistically significant impact on skill improvement rates Based on these findings, the existing single-track program, which focused on skilled workers and managers, was restructured into a three-stage customized curriculum consisting of beginner, skilled worker, and executive management levels. Furthermore, the courses were subdivided by company type (common, material/intermediate goods, and product/ application companies) to strengthen field-oriented practical competencies. The proportion of theoretical and practical sessions was also adjusted according to each stage to simultaneously enhance satisfaction with training duration and maximize educational effectiveness. By differentiating the ratio of theoretical to practical hours at each training stage, the revised curriculum improved satisfaction with operation time while maximizing educational outcomes. This study provides empirical evidence to enhance the effectiveness of in-service training and offers both academic and practical implications by presenting a systematic approach to talent development policies within the carbon industry cluster.
4,500원
6.
2025.12
구독 인증기관 무료, 개인회원 유료
Freight-rate forecasting in the VLCC TD3C market remains challenged by abrupt regime shifts, pronounced volatility, and heterogeneity in real-time signals from oil prices, seaborne trade, vessel operations, and macroeconomic factors; these directly impact freight planning and chartering. This study presents a daily multivariate dataset with 4,267 samples covering 2014-02-01 to 2025-10-08, integrating crude benchmarks, fuel spreads, refinery margins, port congestion, inventory levels by region, plus detailed AIS-derived VLCC activity, speed, and operation states, scaled and split 80/10/10 for training, validation, and testing. The proposed framework combines a PyTorch Transformer—optimized using Optuna for d_model=128, 9 layers, 8 heads, a 14-day input window, and 5-day output—with Monte Carlo Dropout for uncertainty quantification. Diagnosis uses differential entropy and coefficient-of-variation to verify convergence with 90 separate runs, while a Kalman filter (Q=0.001, R=0.01) smooths the forecast trajectory and enhances temporal reliability. Experimental results show baseline Transformer achieves average MAE 5,259.4, MAPE 13.10%, and R²=0.74 across 1-5 day horizons, with volatility quality metrics declining at longer leads. Applying the Kalman filter reduces errors to MAE 4,326.1, MAPE 10.6%, and raises R² to 0.83; timing and extremity components of volatility quality scores are strengthened, providing a more robust basis for operational decisions. Monte Carlo backtesting for 82 Korean VLCCs over 598 trades finds the Kalman-smoothed strategy earns $108.5M (88.9% win rate, Sharpe ratio 0.83), substantially outperforming raw Transformer ($32.9M, 60.5%, 0.24) and random selection (near zero, 49.3%, 0.005). These results highlight the clear economic value added by calibrating uncertainty and post-processing forecasts, transforming predictive reliability into real-world freight portfolio improvement in the tanker market.
4,600원
7.
2025.12
구독 인증기관 무료, 개인회원 유료
The Republic of Korea Armed Forces are implementing various scientific training systems to prepare for future warfare and are seeking advanced integration with AI - driven scientific military innovation. This study proposes the Synthetic Training Environment (STE) as a future-oriented model designed to overcome current limitations in military training. STE provides the foundation for evolving into an integrated, combined arms training system. In addition, the study introduces a data integration framework based on the Defense Training Management System (DTMS). This framework aims to standardize and unify training data across service branches, thereby enabling effective AI interoperability. Future tasks include real-time integration of synthetic and live training, 24/7 data access via the Metaverse, and the establishment of a cyclic system of learning, operation, and evolution. To that end, this research ultimately proposes the CJDSW-MST model - a comprehensive framework linking STE, DTMS, and unmanned combat systems for future-ready, intelligent military training.
4,000원
8.
2025.12
구독 인증기관 무료, 개인회원 유료
This study develops a generative AI-based system for automatically generating scholarly topic descriptions within the OpenAlex database and evaluates its performance. Although OpenAlex provides concise topic descriptions, they lack contextual richness and informational coverage, limiting researchers’ ability to quickly grasp the semantic relevance of each topic. To address this issue, this study generated new descriptions for a total of 4,516 topics by utilizing metadata attributes—topic_id, topic_name, description, and keywords—and compared them with the original descriptions. Multiple large language models (LLMs), including GPT, LLaMA, and Mistral, were employed, and a consistent prompt-engineering scheme was designed to ensure the reproducibility of model comparison. A standardized evaluation framework integrating quantitative and qualitative indicators was proposed. Quantitative evaluation included keyword-based Precision, Recall, and F1 scores, ROUGE-L, Specter2 embedding-based cosine similarity, and BERTScore. Qualitative evaluation was conducted using LLM-based pairwise comparison, assessing Relevance, Coverage, and Clarity, with relative rankings determined through the Elo rating system. Furthermore, the Friedman test and Wilcoxon signed-rank test were applied to verify statistical significance. Experimental results revealed distinctive strengths and weaknesses across models, providing a benchmarking foundation for improving automated content generation in scholarly databases such as OpenAlex. The proposed evaluation framework also offers a reproducible and consistent basis for assessing various generative models, contributing to both academic research and practical system development.
4,900원
9.
2025.12
구독 인증기관 무료, 개인회원 유료
Defect detection in manufacturing processes is a critical requirement for ensuring product reliability and maintaining production stability. As smart manufacturing environments continue to advance, the need for precise and robust vision-based inspection methods has become increasingly significant. This study proposes a hybrid defect analysis framework that integrates YOLOv5-based defect candidate detection with an Attention U-Net–based segmentation module. Experiments conducted on chromate-coated industrial images demonstrate that the proposed framework achieves an accuracy of 0.97, precision of 0.91, recall of 0.89, F1-score of 0.93, and IoU of 0.88, exhibiting stable performance even for small defects and irregular boundaries. The combination of region- of-interest extraction and attention-enhanced pixel-level segmentation improves both computational efficiency and boundary reconstruction quality. The findings extend the applicability of attention-based segmentation to industrial defect inspection and provide practical insights for deploying deep learning–based quality monitoring systems in automated manufacturing environments.
4,000원
10.
2025.12
구독 인증기관 무료, 개인회원 유료
As the unmanned aerial vehicle industry grows, unexplained multirotor crashes continue to increase, and existing preventive maintenance methods have limitations in managing multirotor safety. Safety must be the top priority in multi-copter operations. To address this, real-time monitoring of the multi-copter's flight status during operation is required, along with anomaly detection and immediate response based on flight log information. However, limitations exist in processing anomaly data for each flight control log, necessitating the development of standardized technology to overcome this challenge. In this paper we propose a standardized process for collecting multi-copter flight control logs in real time, classifying the log information by message sets, and extracting key defect detection indicators contained in each message set. Furthermore, the extracted defect detection indicators were validated using various supervised learning models. In our experimental results, we collected flight logs from a multi-copter equipped with a defective propeller and conducted experiments using three defect detection models. The results show an accuracy rate of 0.99. This is the F1-score for the defect detection rate.
4,000원
11.
2025.12
구독 인증기관 무료, 개인회원 유료
The rapid expansion of the fast fashion industry has led to a dramatic increase in textile waste, posing significant environmental and systemic challenges. Although approximately 95% of discarded clothing is technically recyclable, current recycling system remains inefficient due to fragmented collection, manual sorting, limited recycling capabilities, and a lack of integrated data management. This study investigates the structural limitations of Korea’s waste clothing recycling system and proposes optimization strategies grounded in circular economy principles. These strategies, if implemented, have the potential to significantly improve the efficiency and effectiveness of Korea’s textile waste recycling system. Through a comparative analysis of international models― including government-led Extended Producer Responsibility (EPR) systems, digital platform-based collection services, and brand-driven recycling initiatives―the study identifies key bottlenecks in Korea’s current system. The findings highlight the need for a unified and monitored collection infrastructure, the deployment of AI-based automated sorting technologies, and the development of fiber-to-fiber (F2F) recycling processes supported by standardized classification codes and centralized databases. Furthermore, the study emphasizes the importance of real-time data integration across all stages of the recycling chain to enable transparent tracking and performance evaluation. Drawing on successful PET bottle recycling cases, the research outlines a roadmap for transitioning Korea’s textile waste management to a scalable, sustainable circular economy. The study concludes by calling for robust institutional support, legal clarity, and most importantly, cross-sector collaboration. This collaboration is crucial to ensure effective implementation of EPR and long-term resource circulation, and it will require the collective efforts of environmental policymakers, waste management professionals, industry stakeholders, and researchers.
4,900원
12.
2025.12
구독 인증기관 무료, 개인회원 유료
This study addresses the challenge of imputing missing values in incomplete process data collected from high-cost data acquisition environments. Such missingness arises due to insufficient completeness, accuracy, and consistency, which significantly affect the quality of critical-to-quality (CTQ) attributes in manufacturing processes. We systematically evaluate three state-of-the-art imputation methods—Multiple Imputation by Chained Equations (MICE), the machine learning-based missForest algorithm, and a deep learning- based one-dimensional convolutional neural network (1D-CNN)—using real-world industrial data. Our analysis aims to identify the most effective imputation technique for handling complex and noisy process datasets typical in manufacturing settings. The results highlight the strengths and limitations of each method, providing practical guidance for selecting appropriate imputation approaches to improve the reliability of quality prediction and decision-making in industrial applications.
4,500원
13.
2025.12
구독 인증기관 무료, 개인회원 유료
This study verified the moderating effects of personality types (Ego-States) on the relationship between Training Attitude (TA), Training Environment (TE), and Training Performance (TP) among Work-Learning Dual program apprentices. Drawing from Transactional Analysis, personality types were classified into five Ego-States: Critical Parent (CP), Nurturing Parent (NP), Adult (A), Free Child (FC), and Adapted Child (AC). An analysis of 354 apprentices revealed significant differences in TP scores. The FC and A types demonstrated the highest performance (mean scores of 4.11 and 4.05 out of 5, respectively), whereas the CP and NP types recorded lower scores (3.25 and 3.30, respectively). Regarding the moderating effects, TA was found to have a significant effect on TP at the 5% significance level, and the interaction term between TA and personality type also showed a significant effect on TP at the 5% level. Consequently, personality type was identified as a quasi-moderator for TA. Specifically, the positive effect of TA was weakened for the AC type but strengthened for the FC type. In the case of TE, the analysis indicated no significant direct effect on TP at the 5% significance level. However, the interaction term between TE and personality type significantly influenced TP at the 5% level, thereby confirming personality type as a pure moderator for TE. This significant impact was observed specifically for the A and AC types. These findings demonstrate that the pathway to high training performance is not uniform. The results strongly suggest the need for differentiated training management and instructional design tailored to the specific personality profiles of apprentices to maximize program effectiveness.
4,500원
14.
2025.12
구독 인증기관 무료, 개인회원 유료
Underwater sonar image classification is essential for maritime surveillance, autonomous navigation, and underwater target identification, where optical sensing is often restricted by turbidity and light attenuation. To enhance the robustness of sonar-based perception under such challenging conditions, this study proposes a metric-enhanced Vision Transformer (ViT) framework that integrates Siamese-based representation alignment with distance-regularized classification. In the first stage, a Siamese pre-training strategy is employed to align embeddings of positive pairs, encouraging directionally consistent representations that improve class separability even under severe noise and viewpoint variations. In the second stage, the pretrained ViT encoder is frozen, and five classifiers—Linear, Cosine, Proxy, and their Mahalanobis-regularized variants—are systematically evaluated to investigate the effect of embedding normalization and distributional alignment. Experimental results on the UATD dataset demonstrate that the Siamese-trained ViT produces more stable and discriminative features than both ResNet-50 and standard ViT-S. Among the classifiers, the Mahalanobis-regularized cosine classifier achieves the highest, showing significant reductions in misclassification between visually similar classes such as cube and square cage. Overall, the proposed approach highlights the effectiveness of combining ViT with metric learning and covariance-aware distance normalization for underwater sonar image recognition. The results suggest that metric-enhanced transformers offer a robust and generalizable foundation for sonar-based perception in real maritime environments.
4,000원
15.
2025.12
구독 인증기관 무료, 개인회원 유료
Grease consistency is a critical quality factor in industrial lubrication processes, as it significantly affects mechanical performance, operational stability, and product durability. In grease manufacturing, fluctuations in process variables such as feed temperature, evaporation time, flow rate, and environmental conditions can cause inconsistencies in quality, which may lead to operational defects or increased maintenance costs. To address this challenge, this study proposes a predictive modeling approach for forecasting grease consistency with the aim of enhancing process quality. Real manufacturing process data were collected from a grease production facility, and irrelevant or highly correlated variables were eliminated through multicollinearity analysis and dimensionality reduction. Multiple machine learning regression techniques were applied and evaluated to identify the most effective model for predicting grease consistency. Through systematic comparison, the final predictive model was developed to provide accurate consistency estimation based on selected process variables. The proposed model enables proactive quality control by allowing consistency deviations to be detected early, thereby supporting process optimization and decision-making in manufacturing environments. This research demonstrates the applicability of data-driven predictive modeling in the grease industry and contributes to the development of intelligent quality management strategies in modern manufacturing. The findings suggest that machine learning-based consistency prediction can play a key role in improving production efficiency and ensuring stable product performance.
4,000원
